class_docstr: Text Processor class implement NER, POS tag, lexical tree parsing.
  EnProcessor is designed by single instance mode.
docstr: Tokenize one sentence.
  
  :param str text:
  :param bool split_by_space: whether tokenize sentence by split space
  :return: tokens
docstr_tok: ["Tokenize","one","sentence",".",":","param","str","text",":",":","param","bool","split_by_space",":","whether","tokenize","sentence","by","split","space",":","return",":","tokens"]
code: def tokenize_one_sent(self, text, split_by_space=False):
      r"""
      Tokenize one sentence.
  
      :param str text:
      :param bool split_by_space: whether tokenize sentence by split space
      :return: tokens
  
      """
      assert isinstance(text, str)
  
      if split_by_space:
          return text.split(" ")
      else:
          self.check_initialized()
          return [
              word.text.replace("''", '"').replace("``", '"')
              for word in self.nlp.tokenizer(text)
              if word.text != " " * len(word)
          ]
  
imports: import re
  import nltk
  import spacy
  import threading
  from functools import reduce
  from spacy.tokens import Doc
  from .nltk_res_load import *
  from ..settings import *
label_desc: Use image node_id 6 for calling the EnProcessor obj's underlying member method code with example usage: obj.tokenize_one_sent(text, split_by_space) and returns: text, unknown